Target encoding plays a central role when learning Convolutional Neural Networks. In this
realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so …

Automatic recognition of different tissue types in histological images is an essential part in
the digital pathology toolbox. Texture analysis is commonly used to address this problem; …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

Partial multi-label learning (PML) is an emerging weakly supervised learning framework,
where each training example is associated with multiple candidate labels which are only …

In partial label learning, each training example is associated with a set of candidate labels
among which only one is the ground-truth label. The common strategy to induce predictive …

Purpose: This study aims to characterize quantitative optical coherence tomography
angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate …

This paper proposes a sensor and actuator fault diagnosis method for small pressurized
water reactors (SPWRs), with an innovative labeled fault dictionary established to map …

Radars are expected to become the main sensors in various civilian applications, ranging
from health-care monitoring to autonomous driving. Their success is mainly due to the …

The scalp electroencephalogram (EEG)-based epileptic seizure/nonseizure detection has
been comprehensively studied, and fruitful achievements have been reported in the past …

In this paper, a noncontact breathing disorder recognition system has been proposed for
identifying irregular breathing patterns. The proposed system consists of a Doppler radar …